Electoral-Integrity-in-2016-US-Election
OpenML dataset with id 43763
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Full work available at URL: https://api.openml.org/data/v1/download/22102588/Electoral-Integrity-in-2016-US-Election.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 85 (numeric: 23, symbolic: 0 and in total binary: 0 )
Number of instances: 726
Number of instances with missing values: 726
Number of missing values: 4,001
Context
Electoral integrity refers to international standards and global norms governing the appropriate conduct of elections. These standards have been endorsed in a series of authoritative conventions, treaties, protocols, and guidelines by agencies of the international community and apply universally to all countries throughout the electoral cycle, including during the pre-electoral period, the campaign, on polling day, and in its aftermath.
Content
The Perceptions of Electoral Integrity (PEI) survey asks experts to evaluate elections according to 49 indicators, grouped into eleven categories reflecting the whole electoral cycle. The PEI dataset is designed to provide a comprehensive, systematic and reliable way to monitor the quality of elections worldwide. It includes disaggregated scores for each of the individual indicators, summary indices for the eleven dimensions of electoral integrity, and a PEI index score out of 100 to summarize the overall integrity of the election.
Acknowledgements
This study was conducted by Pippa Norris, Alessandro Nai, and Max Grmping for Harvard University's Electoral Integrity Project.
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